code: def check_feature_screening(
      screening_percentile, mask_img, is_classification, verbose=0
  ):
      """Check feature screening method.
  
      Turns floats between 1 and 100 into SelectPercentile objects.
  
      Parameters
      ----------
      screening_percentile : float in the interval [0, 100]
          Percentile value for :term:`ANOVA` univariate feature selection.
          A value of 100 means 'keep all features'.
          This percentile is expressed
          w.r.t the volume of a standard (MNI152) brain, and so is corrected
          at runtime by premultiplying it with the ratio of the volume of the
          mask of the data and volume of a standard brain.
  
      mask_img : nibabel image object
          Input image whose :term:`voxel` dimensions are to be computed.
  
      is_classification : bool
          If is_classification is True, it indicates that a classification task
          is performed. Otherwise, a regression task is performed.
  
      verbose : int, default=0
          Verbosity level.
  
      Returns
      -------
      selector : SelectPercentile instance
         Used to perform the :term:`ANOVA` univariate feature selection.
  
      """
      f_test = f_classif if is_classification else f_regression
  
      if screening_percentile == 100 or screening_percentile is None:
          return None
      elif not (0.0 <= screening_percentile <= 100.0):
          raise ValueError(
              "screening_percentile should be in the interval"
              f" [0, 100], got {screening_percentile:g}"
          )
      else:
          # correct screening_percentile according to the volume of the data mask
          screening_percentile_ = adjust_screening_percentile(
              screening_percentile, mask_img, verbose=verbose
          )
  
          return SelectPercentile(
              f_test, percentile=int(screening_percentile_)
          )
  
file_path: nilearn/nilearn/_utils/param_validation.py
imports: import numbers
  import warnings
  import numpy
  from sklearn.feature_selection import SelectPercentile, f_classif, f_regression
  from .niimg import _get_data
label_desc: Use image node_id 4 for calling a global function with example usage: check_feature_screening(screening_percentile, mask_img, is_classification, verbose) and returns: None, SelectPercentile